chore: rm legacy llm client

packages/llms/src/OpenAIClient.ts

@@ -1,192 +0,0 @@
-/**
- * OpenAI Client implementation
- * @note This client is only for demonstrating how to implement a LLM client.
- * @note Use OpenAILenientClient instead.
- */
-import { InvokeError, InvokeErrorType } from './errors'
-import type { InvokeResult, LLMClient, LLMConfig, Message, Tool } from './types'
-import { modelPatch, zodToOpenAITool } from './utils'
-
-/**
- * @deprecated Use OpenAILenientClient instead.
- */
-export class OpenAIClient implements LLMClient {
-	config: LLMConfig
-
-	constructor(config: LLMConfig) {
-		this.config = config
-	}
-
-	async invoke(
-		messages: Message[],
-		tools: Record<string, Tool>,
-		abortSignal?: AbortSignal
-	): Promise<InvokeResult> {
-		// 1. Convert tools to OpenAI format
-		const openaiTools = Object.entries(tools).map(([name, tool]) => zodToOpenAITool(name, tool))
-
-		// 2. Call API
-		let response: Response
-		try {
-			response = await fetch(`${this.config.baseURL}/chat/completions`, {
-				method: 'POST',
-				headers: {
-					'Content-Type': 'application/json',
-					Authorization: `Bearer ${this.config.apiKey}`,
-				},
-				body: JSON.stringify(
-					modelPatch({
-						model: this.config.model,
-						temperature: this.config.temperature,
-						messages,
-
-						tools: openaiTools,
-						// tool_choice: 'required',
-						tool_choice: { type: 'function', function: { name: 'AgentOutput' } },
-
-						// model specific params
-
-						// reasoning_effort: 'minimal',
-						// verbosity: 'low',
-						parallel_tool_calls: false,
-					})
-				),
-				signal: abortSignal,
-			})
-		} catch (error: unknown) {
-			// Network error
-			throw new InvokeError(InvokeErrorType.NETWORK_ERROR, 'Network request failed', error)
-		}
-
-		// 3. Handle HTTP errors
-		if (!response.ok) {
-			const errorData = await response.json().catch()
-			const errorMessage =
-				(errorData as { error?: { message?: string } }).error?.message || response.statusText
-
-			if (response.status === 401 || response.status === 403) {
-				throw new InvokeError(
-					InvokeErrorType.AUTH_ERROR,
-					`Authentication failed: ${errorMessage}`,
-					errorData
-				)
-			}
-			if (response.status === 429) {
-				throw new InvokeError(
-					InvokeErrorType.RATE_LIMIT,
-					`Rate limit exceeded: ${errorMessage}`,
-					errorData
-				)
-			}
-			if (response.status >= 500) {
-				throw new InvokeError(
-					InvokeErrorType.SERVER_ERROR,
-					`Server error: ${errorMessage}`,
-					errorData
-				)
-			}
-			throw new InvokeError(
-				InvokeErrorType.UNKNOWN,
-				`HTTP ${response.status}: ${errorMessage}`,
-				errorData
-			)
-		}
-
-		const data = await response.json()
-
-		// 4. Check finish_reason
-		const choice = data.choices?.[0]
-		if (!choice) {
-			throw new InvokeError(InvokeErrorType.UNKNOWN, 'No choices in response', data)
-		}
-
-		switch (choice.finish_reason) {
-			case 'tool_calls':
-				// ✅ Normal
-				break
-			case 'length':
-				// ⚠️ Token limit reached
-				throw new InvokeError(
-					InvokeErrorType.CONTEXT_LENGTH,
-					'Response truncated: max tokens reached',
-					data
-				)
-			case 'content_filter':
-				// ❌ Content filtered
-				throw new InvokeError(
-					InvokeErrorType.CONTENT_FILTER,
-					'Content filtered by safety system',
-					data
-				)
-			case 'stop':
-				// ❌ Did not call tool (we require tool call)
-				throw new InvokeError(InvokeErrorType.NO_TOOL_CALL, 'Model did not call any tool', data)
-			default:
-				throw new InvokeError(
-					InvokeErrorType.UNKNOWN,
-					`Unexpected finish_reason: ${choice.finish_reason}`,
-					data
-				)
-		}
-
-		// 5. Parse tool call
-		const toolCall = choice.message?.tool_calls?.[0]
-		if (!toolCall) {
-			throw new InvokeError(InvokeErrorType.NO_TOOL_CALL, 'No tool call found in response', data)
-		}
-
-		const toolName = toolCall.function.name
-		const tool = tools[toolName]
-		if (!tool) {
-			throw new InvokeError(InvokeErrorType.UNKNOWN, `Tool ${toolName} not found`, data)
-		}
-
-		// 6. Parse and validate arguments
-		let toolArgs: unknown
-		try {
-			toolArgs = JSON.parse(toolCall.function.arguments)
-		} catch (e) {
-			throw new InvokeError(InvokeErrorType.INVALID_TOOL_ARGS, 'Invalid JSON in tool arguments', e)
-		}
-
-		// Validate against zod schema
-		const validation = tool.inputSchema.safeParse(toolArgs)
-		if (!validation.success) {
-			throw new InvokeError(
-				InvokeErrorType.INVALID_TOOL_ARGS,
-				`Tool arguments validation failed: ${validation.error.message}`,
-				validation.error
-			)
-		}
-
-		// 7. Execute tool
-		let toolResult: unknown
-		try {
-			toolResult = await tool.execute(validation.data)
-		} catch (e) {
-			throw new InvokeError(
-				InvokeErrorType.TOOL_EXECUTION_ERROR,
-				`Tool execution failed: ${(e as Error).message}`,
-				e
-			)
-		}
-
-		// 8. Return result (including cache tokens)
-		return {
-			toolCall: {
-				// id: toolCall.id,
-				name: toolName,
-				args: validation.data as Record<string, unknown>,
-			},
-			toolResult,
-			usage: {
-				promptTokens: data.usage?.prompt_tokens ?? 0,
-				completionTokens: data.usage?.completion_tokens ?? 0,
-				totalTokens: data.usage?.total_tokens ?? 0,
-				cachedTokens: data.usage?.prompt_tokens_details?.cached_tokens,
-				reasoningTokens: data.usage?.completion_tokens_details?.reasoning_tokens,
-			},
-			rawResponse: data,
-		}
-	}
-}

packages/llms/src/OpenAICompatibleClient.ts

@@ -90,7 +90,6 @@ export class OpenAIClient implements LLMClient {
 		// 4. Parse and validate response
 		const data = await response.json()
 
-		// Basic validation before normalize (these are structural issues, not format issues)
 		const choice = data.choices?.[0]
 		if (!choice) {
 			throw new InvokeError(InvokeErrorType.UNKNOWN, 'No choices in response', data)
@@ -116,7 +115,7 @@ export class OpenAIClient implements LLMClient {
 				)
 		}
 
-		// Apply normalizeResponse if provided (for fixing format issues like wrong tool name)
+		// Apply normalizeResponse if provided (for fixing format issues automatically)
 		const normalizedData = options?.normalizeResponse ? options.normalizeResponse(data) : data
 		const normalizedChoice = (normalizedData as any).choices?.[0]